本文選題：捷聯(lián)慣導(dǎo)　切入點(diǎn)：初始對(duì)準(zhǔn)　出處：《哈爾濱工程大學(xué)》2014年碩士論文

【摘要】：粒子濾波(Particle Filter,簡(jiǎn)稱PF)是一種性能優(yōu)越的非線性濾波算法。它對(duì)系統(tǒng)的狀態(tài)方程和量測(cè)方程以及噪聲統(tǒng)計(jì)特性均未加任何限制,因而相對(duì)于傳統(tǒng)的非線性濾波具有更寬廣的應(yīng)用范圍。而且它突破了卡爾曼濾波體系下非線性濾波(EKF、UKF、CKF)的架構(gòu)束縛,摒棄了對(duì)狀態(tài)變量均值和方差估計(jì)的思想,轉(zhuǎn)而通過(guò)從后驗(yàn)概率密度中抽取采樣粒子,并對(duì)其進(jìn)行迭代的預(yù)測(cè)和更新,來(lái)不斷逼近真實(shí)的后驗(yàn)概率密度分布,從而更加的貼近最優(yōu)估計(jì)的本質(zhì)。粒子濾波具有濾波精度高,收斂速度快等優(yōu)點(diǎn),使得它已經(jīng)成為了在處理非線性、非高斯系統(tǒng)下?tīng)顟B(tài)濾波和參數(shù)估計(jì)的主流濾波算法.本文圍繞粒子濾波在捷聯(lián)慣導(dǎo)大方位失準(zhǔn)角背景下展開(kāi)了如下的研究工作:首先,根據(jù)歐拉平臺(tái)誤差角推導(dǎo)了大方位失準(zhǔn)角條件下捷聯(lián)慣導(dǎo)系統(tǒng)初始對(duì)準(zhǔn)的誤差方程。在研究貝葉斯信號(hào)處理和蒙特卡洛(MonteCarlo)積分的的基礎(chǔ)上,引出序貫重要性重采樣(Sequential Importance Sample)粒子濾波算法,并給出目標(biāo)跟蹤模型下的PF和UKF仿真對(duì)比。其次,針對(duì)標(biāo)準(zhǔn)粒子濾波算法中的缺點(diǎn)和不足,提出以下改進(jìn)方法:一是標(biāo)準(zhǔn)粒子濾波當(dāng)中直接選取先驗(yàn)概率密度函數(shù)作為重要性密度函數(shù)進(jìn)行采樣粒子,導(dǎo)致最新時(shí)刻的量測(cè)信息丟失,使得采樣的粒子過(guò)分依賴于狀態(tài)模型,當(dāng)似然概率密度呈現(xiàn)尖峰狀態(tài)或是位于先驗(yàn)概率密度函數(shù)尾部的時(shí)候很容易造成粒子退化。提出根據(jù)最新時(shí)刻的量測(cè)信息,給予重要性密度函數(shù)有目的的調(diào)整、修正,從而使得重要性密度函數(shù)能夠最大程度上的向后驗(yàn)概率密度分布偏移。本文設(shè)計(jì)的算法是使用基于EKF、UKF、CKF濾波估計(jì)之后的均值和方差來(lái)產(chǎn)生了新的采樣粒子集群。給出分段非線性模型下的PF、EKPF、UPF、CPF的對(duì)比仿真分析。二是高維狀態(tài)估計(jì)中所需粒子數(shù)成級(jí)倍數(shù)增長(zhǎng),因而計(jì)算延遲,實(shí)時(shí)性不夠理想的情況,提出動(dòng)態(tài)調(diào)節(jié)粒子數(shù),減少計(jì)算量。具體做法是將自適應(yīng)技術(shù)引入到粒子濾波的重采樣之前,根據(jù)上一時(shí)刻對(duì)信號(hào)的估計(jì)精度來(lái)確定下一時(shí)刻估計(jì)所需要的粒子數(shù)。設(shè)計(jì)在目標(biāo)跟蹤模型下的仿真來(lái)對(duì)比APF和PF的濾波性能,驗(yàn)證自適應(yīng)的有效性。三是將CPF和APF結(jié)合形成ACPF算法,新算法既通過(guò)CKF設(shè)計(jì)重要性密度函數(shù),提高了采樣效率;又根據(jù)上一時(shí)刻的估計(jì)狀態(tài)預(yù)測(cè)下一時(shí)刻所需要的粒子數(shù),實(shí)現(xiàn)了粒子動(dòng)態(tài)調(diào)節(jié),減少了計(jì)算量。最后在飛行器機(jī)動(dòng)飛行模型中,驗(yàn)證ACPF的有效性。最后仿真在捷聯(lián)慣導(dǎo)初始對(duì)準(zhǔn)非線性誤差模型下進(jìn)行,給出ACPF和PF的仿真對(duì)比分析,證明算法改進(jìn)的正確性和有效性。
[Abstract]:Particle filter Particle filter (PFR) is a nonlinear filtering algorithm with excellent performance. It has no restrictions on the state equation, measurement equation and noise statistical characteristics of the system. Therefore, compared with the traditional nonlinear filtering, it has a wider range of applications. Moreover, it breaks through the structural shackles of nonlinear filtering in Kalman filtering system and abandons the idea of estimating the mean and variance of state variables. Instead, sampling particles are extracted from the posterior probability density and iterated to predict and update them to continuously approach the true posterior probability density distribution, which is closer to the essence of the optimal estimation. Particle filter has high filtering accuracy. The advantages of fast convergence have made it more and more effective in dealing with nonlinearity. The main filtering algorithms of state filtering and parameter estimation in non-#china_person0# system. This paper focuses on particle filtering in the background of large azimuth misalignment angle of sins. According to the error angle of Euler platform, the error equation of sins initial alignment under the condition of large azimuth misalignment is derived. Based on the study of Bayesian signal processing and Monte Carlo integral, The sequential importance resampling Importance sampling (Sequential Importance sample) particle filter algorithm is introduced, and the simulation comparison between PF and UKF in the target tracking model is given. Secondly, the shortcomings and shortcomings of the standard particle filter algorithm are discussed. The following improved methods are proposed: first, the priori probability density function is directly selected as the importance density function to sample the particles in the standard particle filter, which results in the loss of measurement information at the latest time. When the likelihood probability density is in a peak state or at the end of a priori probability density function, it is easy to cause particle degradation. Give importance density function purposeful adjustment, correction, Therefore, the importance density function can offset the posterior probability density distribution to the maximum extent. The algorithm designed in this paper uses the mean value and variance after estimation based on EKFU UKFU CKF filter to generate a new sample particle cluster. The comparison and simulation analysis of the PFEK PFU CPF under the piecewise nonlinear model are given. The second is the increase of the number of particles required in the high dimensional state estimation. Therefore, when computing delay and real time are not ideal, a dynamic adjustment of particle number is proposed to reduce the computational load. The specific method is to introduce adaptive technology to the resampling of particle filter. The number of particles needed to estimate the signal at the next time is determined according to the estimation accuracy of the signal at the previous time. Simulation based on the target tracking model is designed to compare the filtering performance of APF and PF. The third is to combine CPF and APF to form ACPF algorithm. The new algorithm not only improves the sampling efficiency by designing the importance density function of CKF, but also predicts the number of particles needed at the next moment according to the estimated state of the previous time. Finally, the effectiveness of ACPF is verified in the flight model of aircraft maneuvering. Finally, the simulation is carried out under the nonlinear error model of initial alignment of sins, and the comparison between ACPF and PF is given. The correctness and validity of the improved algorithm are proved.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN96;TN713

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